{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LRKmRzoWeJkbe7x3XCfaFYE8j6qCYF6vx2t7eYFriairLHmmxCSFvQwnspWDv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2141b8c1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 随机生成1000个点，围绕在y=0.1x+0.3的直线周围\n",
    "num_points = 1000\n",
    "vectors_set = []\n",
    "for i in range(num_points):\n",
    "    x1 = np.random.normal(0.0, 0.55)\n",
    "    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)\n",
    "    vectors_set.append([x1, y1])\n",
    "\n",
    "# 生成一些样本\n",
    "x_data = [v[0] for v in vectors_set]\n",
    "y_data = [v[1] for v in vectors_set]\n",
    "\n",
    "plt.scatter(x_data,y_data,c='r')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W = [ 0.96539688] b = [ 0.] loss = 0.297884\n",
      "W = [ 0.71998411] b = [ 0.28193575] loss = 0.112606\n",
      "W = [ 0.54009342] b = [ 0.28695393] loss = 0.0572231\n",
      "W = [ 0.41235447] b = [ 0.29063231] loss = 0.0292957\n",
      "W = [ 0.32164571] b = [ 0.2932443] loss = 0.0152131\n",
      "W = [ 0.25723246] b = [ 0.29509908] loss = 0.00811188\n",
      "W = [ 0.21149193] b = [ 0.29641619] loss = 0.00453103\n",
      "W = [ 0.17901111] b = [ 0.29735151] loss = 0.00272536\n",
      "W = [ 0.15594614] b = [ 0.29801565] loss = 0.00181483\n",
      "W = [ 0.13956745] b = [ 0.29848731] loss = 0.0013557\n",
      "W = [ 0.12793678] b = [ 0.29882219] loss = 0.00112418\n",
      "W = [ 0.11967772] b = [ 0.29906002] loss = 0.00100743\n",
      "W = [ 0.11381286] b = [ 0.29922891] loss = 0.000948558\n",
      "W = [ 0.10964818] b = [ 0.29934883] loss = 0.000918872\n",
      "W = [ 0.10669079] b = [ 0.29943398] loss = 0.000903903\n",
      "W = [ 0.10459071] b = [ 0.29949448] loss = 0.000896354\n",
      "W = [ 0.10309943] b = [ 0.29953739] loss = 0.000892548\n",
      "W = [ 0.10204045] b = [ 0.29956791] loss = 0.000890629\n",
      "W = [ 0.10128847] b = [ 0.29958954] loss = 0.000889661\n",
      "W = [ 0.10075447] b = [ 0.29960492] loss = 0.000889173\n",
      "W = [ 0.10037527] b = [ 0.29961586] loss = 0.000888927\n"
     ]
    }
   ],
   "source": [
    "# 生成1维的W矩阵，取值是[-1,1]之间的随机数\n",
    "W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')\n",
    "# 生成1维的b矩阵，初始值是0\n",
    "b = tf.Variable(tf.zeros([1]), name='b')\n",
    "# 经过计算得出预估值y\n",
    "y = W * x_data + b\n",
    "\n",
    "# 以预估值y和实际值y_data之间的均方误差作为损失\n",
    "loss = tf.reduce_mean(tf.square(y - y_data), name='loss')\n",
    "# 采用梯度下降法来优化参数\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.5)\n",
    "# 训练的过程就是最小化这个误差值\n",
    "train = optimizer.minimize(loss, name='train')\n",
    "\n",
    "sess = tf.Session()\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "\n",
    "# 初始化的W和b是多少\n",
    "print (\"W =\", sess.run(W), \"b =\", sess.run(b), \"loss =\", sess.run(loss))\n",
    "# 执行20次训练\n",
    "for step in range(20):\n",
    "    sess.run(train)\n",
    "    # 输出训练好的W和b\n",
    "    print (\"W =\", sess.run(W), \"b =\", sess.run(b), \"loss =\", sess.run(loss))\n",
    "writer = tf.train.SummaryWriter(\"./tmp\", sess.graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hkkVCd3e3950lPeOdeJZMokZVTblc2bOdFYmMRQ0NZnFLi5YS6hSJBSHEtKcS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\nzEq5WDAzM7NSLhbMzMyslIsFMzMzK/U/DAltZigwxHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2141f1c66d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_data,y_data,c='r')\n",
    "plt.plot(x_data,sess.run(W)*x_data+sess.run(b))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', 'C:\\\\Anaconda3\\\\python35.zip', 'C:\\\\Anaconda3\\\\DLLs', 'C:\\\\Anaconda3\\\\lib', 'C:\\\\Anaconda3', 'C:\\\\Anaconda3\\\\lib\\\\site-packages', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\Sphinx-1.4.6-py3.5.egg', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\protobuf-3.1.0.post1-py3.5.egg', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32\\\\lib', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\Pythonwin', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\setuptools-27.2.0-py3.5.egg', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\IPython\\\\extensions', 'C:\\\\Users\\\\user\\\\.ipython']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print (sys.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
